Multimodal Probabilistic Person Tracking and Identification in Smart Spaces

In this thesis, a new methodology is introduced for the multimodal tracking and identification of multiple persons by seeking and integrating reliable ID cues whenever they become observable. The method opportunistically integrates person-specific identification cues that can only sparsely be observed for each person over time and keeps track of the location of identified persons while ID cues are not available

The CLEAR 2007 Evaluation

Abstract. This paper is a summary of the 2007 CLEAR Evaluation on the Classification of Events, Activities, and Relationships which took place in early 2007 and culminated with a two-day workshop held in May 2007. CLEAR is an international effort to evaluate systems for the perception of people, their activities, and interactions. In its second year, CLEAR has developed a following from the computer vision and speech communities, spawning a more multimodal perspective of research eval-uation. This paper describes the evaluation tasks, including metrics and databases used, and discusses the results achieved. The CLEAR 2007 tasks comprise person, face, and vehicle tracking, head pose estimation, as well as acoustic scene analysis. These include subtasks performed in the visual, acoustic and audio-visual domains for meeting room and surveillance data.

Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

<p>Abstract</p> <p>Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.</p

Multi-Level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking

Abstract. In this paper, two multimodal systems for the tracking of multiple users in smart environments are presented. The first is a multiview particle filter tracker using foreground, color and special upper body detection and person region features. The other is a wide angle overhead view person tracker relying on foreground segmentation and model-based blob tracking. Both systems are completed by a joint probabilistic data association filter-based source localizer using the input from several microphone arrays. The systems are designed to estimate the 3D scene locations of room occupants and are evaluated based on their precision in estimating person locations, their accuracy in recognizing person configurations and their ability to consistently keep track identities over time. The trackers are extensively tested and compared, for each separate modality and for the combined modalities, on the CLEAR 2007 Evaluation Database

Multiple Object Tracking Performance Metrics and Evaluation in a Smart Room Environment

Simultaneous tracking of multiple persons in real world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. We also present a novel system for tracking multiple users i